ESPressoscope: a small and powerful platform for in situ microscopy
Li, E.; Saggiomo, V.; Ouyang, W.; Prakash, M.; Diederich, B.
Show abstract
Microscopy is essential for detecting, identifying, analyzing, and measuring small objects. Access to modern microscopy equipment is crucial for scientific research, especially in the biomedical and analytical sciences. However, the high cost of equipment, limited availability of parts, and challenges associated with transporting equipment often limit the accessibility and operational capabilities of these tools, particularly in field sites and other remote or resource-limited settings. Thus, there is a need for affordable and accessible alternatives to traditional microscopy systems. We address this challenge by investigating the feasibility of using a simple microcontroller board not only as a portable and field-ready digital microscope, but furthermore as a versatile platform which can easily be adapted to a variety of imaging applications. By adding a few external components, we demonstrate that a low-cost ESP32 camera board can be used to build an autonomous in situ platform for digital time-lapse imaging of cells. This platform, which we call the ESPressoscope, can be adapted to applications ranging from monitoring incubator cell cultures in the lab to observing ecological phenomena in the sea, and it can be adapted for other techniques such as microfluidics or spectrophotometry. The ESPressoscope achieves a low power consumption and small size, which makes it ideal for field research in environments and applications where microscopy was previously infeasible. Its Wi-Fi connectivity enables integration with external image processing and storage systems, including on cloud platforms when internet access is available. Finally, we present several web browser-based tools to help users operate and manage the ESPressoscopes software. Our findings demonstrate the potential for low-cost, portable microscopy solutions to enable new and more accessible experiments for biological and analytical applications.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Long-term imaging of three-dimensional hyphal development using the ePetri dish 97%
- An integrated multi-wavelength SCATTIRSTORM microscope combining TIRFM and IRM modalities for imaging cellulases and other processive enzymes 97%
- User-friendly Oblique Plane Microscopy on a fully functional commercially available microscope base 97%
Similar papers in this journal
- The K2: Open-source simultaneous triple-color TIRF microscope for live-cell and single-molecule imaging 96%
- The Incubot: A 3D Printer-Based Microscope for Long-Term Live Cell Imaging within a Tissue Culture Incubator 95%
- OptoPi: An open source flexible platform for the analysis of small animal behaviour 95%
Similar papers in this journal
- TWINKLE: An open-source two-photon microscope for teaching and research 97%
- Facile assembly of an affordable miniature multicolor fluorescence microscope made of 3D-printed parts enables detection of single cells 97%
- A framework to enhance the Signal-to-Noise Ratio for quantitative fluorescence microscopy 96%
Similar papers in this journal
- UC2 - A Versatile and Customizable low-cost 3D-printed Optical Open-Standard for microscopic imaging 97%
- Brain-wide imaging of an adult vertebrate with image transfer oblique plane microscopy 96%
- Full three-dimensional imaging deep through multicellular thick samples with subcellular resolution by structured illumination microscopy and adaptive optics 96%
Similar papers in this journal
- Ultra-thin fluorocarbon foils optimize multiscale imaging of three-dimensional native and optically cleared specimens 95%
- MEMS enabled miniaturized light-sheet microscopy with all optical control 95%
- A low-cost smartphone fluorescence microscope for research, life science education, and STEM outreach 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.